A Recipe For Building a Compliant Real Estate Chatbot
Navid Madani, Anusha Bagalkotkar, Supriya Anand, Gabriel Arnson, Rohini K. Srihari, Kenneth Joseph
Abstract
In recent years, there has been significant effort to align large language models with human preferences. This work focuses on developing a chatbot specialized in the real estate domain, with an emphasis on incorporating compliant behavior to ensure it can be used without perpetuating discriminatory practices like steering and redlining, which have historically plagued the real estate industry in the United States. Building on prior work, we present a method for generating a synthetic general instruction-following dataset, along with safety data. Through extensive evaluations and benchmarks, we fine-tuned a llama-3-8B-instruct model and demonstrated that we can enhance it’s performance significantly to match huge closed-source models like GPT-4o while making it safer and more compliant. We open-source the model, data and code to support further development and research in the community
BibTeX
@inproceedings{madani-etal-2025-recipe,
title = "A Recipe For Building a Compliant Real Estate Chatbot",
author = "Madani, Navid and
Bagalkotkar, Anusha and
Anand, Supriya and
Arnson, Gabriel and
Srihari, Rohini K. and
Joseph, Kenneth",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.18/",
pages = "213--235"
}